Technical Courses, Books, and Tutorials on Artificial Intelligence, Deep Learning and Machine Learning for AI Developers
I've been keeping a running list of highly recommended and informative resources that I have come across while learning more about AI and Machine Learning. Most of these are technical resources intended for experienced software engineers, developers, and coders. The majority of these resources are free and for any that are not, I've added (paid) to the title to distinguish.
I hope you find these resources useful as well and if you have any to add that I'm missing please email me at duncan@openshiro.com or submit a PR to update the source code for this list.
PromptShiro is a tool suite for prompt engineering, model versioning, quantitative testing, performance monitoring, and fine-tuning developed I developed at OpenShiro. Learn more about OpenShiro.
- Intro to Large Language Models - Andrej Karpathy - OpenAI
- CS50's Introduction to Artificial Intelligence with Python - Harvard
- Practical Deep Learning for Coders - fast.ai
- Introduction to Reinforcement Learning - David Silver
- Learn PyTorch for deep learning in a day - Daniel Bourke
- Machine Learning Crash Course with TensorFlow APIs - Google
- Intro to TensorFlow for Deep Learning - Google
- Making Friends with Machine Learning - Google
- From Deep Learning Foundations to Stable Diffusion - fast.ai
- Spinning Up in Deep RL - OpenAI
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning - ** DeepLearning.AI
- Neural Networks and Deep Learning - ** DeepLearning.AI
- Machine Learning for Everybody - FreeCodeCamp
- Natural Language Processing - Hugging Face
- Deep Reinforcement Learning - Hugging Face
- Machine Learning with Python - ** IBM
- Full Stack Deep Learning - The Full Stack
- Supervised Machine Learning: Regression and Classification - ** DeepLearning.AI
- Advanced Learning Algorithms - ** Stanford
- Unsupervised Learning, Recommenders, Reinforcement Learning - ** Stanford
- Transformers for Audio - Hugging Face
- Artificial Intelligence A-Z 2023: Build 5 AI incl. ChatGPT - (paid) SuperDataScience
- Deep Learning Specialization - *** DeepLearning.AI
- Natural Language Processing Specialization - *** DeepLearning.AI
- TensorFlow: Data and Deployment Specialization - *** DeepLearning.AI
- TensorFlow Developer Professional Certificate - *** DeepLearning.AI
- Generative Adversarial Networks (GANs) Specialization - *** DeepLearning.AI
- TensorFlow: Advanced Techniques Specialization - *** DeepLearning.AI
- Generative AI with Large Language Models - *** AWS /DeepLearning.AI
- Let's build GPT: from scratch, in code, spelled out - Andrej Karpathy
- Intro to Machine Learning - Kaggle
- Getting Started with Machine Learning in Python - STX Next
- Intermediate Machine Learning - Kaggle
- Machine Learning & Natural Language Processing Tutorials - Machine Learning is Fun
- Feature Engineering - Kaggle
- A Neural Network in 11 lines of Python - i am trask
- An Introduction to different Types of Convolutions in Deep Learning - Paul-Louis Prove
- TensorFlow Official Tutorials for Beginners and for Experts - TensorFlow
- PyTorch Official Tutorials - PyTorch
- Deep Learning for Coders with fastai and PyTorch - fast.ai
- Dive into Deep Learning - Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola
- Deep Learning with Python (Second Edition) - (paid) Francois Chollet
- Grokking Deep Learning - (paid) Andrew Trask
- Deep Learning: An MIT Press Book - (paid textbook, free eBook) Ian Goodfellow, Yoshua Bengio, Aaron Courville
- An Introduction to Statistical Learning: with Applications in Python - (paid textbook, free eBook) Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, Jonathan Taylor
- An Introduction to Statistical Learning: with Applications in R (Second Edition) - (paid textbook, free eBook) Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani
- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (Second Edition) - (paid) - Aurelien Geron
- Deep Learning from Scratch: Building with Python from First Principles - (paid) Seth Weidman
- Reinforcement Learning: An Introduction MIT Press - Richard S. Sutton, Andrew G. Barto
- Essence of Linear Algebra - 3Blue1Brown
- A friendly Introduction to Linear Algebra for ML - Google
- Linear Algebra for Machine Learning and Data Science - DeepLearning.ai
- Calculus for Machine Learning and Data Science - ** DeepLearning.AI
- Introductory Ideas in Probability - 3Blue1Brown
- Probability & Statistics for Machine Learning & Data Science - ** DeepLearning.AI
- Statistical Learning with Python - Stanford
- Statistical Learning with R - Stanford
- CS50's: Introduction to Computer Science - Harvard
- CS50's Introduction to Programming with Python - Harvard
- Introduction to Computer Science and Programming Using Python - MIT
- Become a Better Software Developer - (free + paid) ArjanCodes
- 100 Days of Code: The Complete Python Pro Bootcamp for 2023 - (paid) London App Brewery
- Intro to Computer Science - Python - Khan Academy
- Automate the Boring Stuff with Python - (free eBook, paid course) - Al Sweigart
- CS231n Deep Learning for Computer Vision - Stanford
- CS224n Natural Language Processing with Deep Learning - Stanford
- Deep Learning and Artificial Intelligence Lectures - MIT
- Meta-Learning and Self-Play - Ilya Sutskever - OpenAI
- Deep Learning State of the Art (2020) - MIT
- TensorFlow at Google I/O 2019 - Google
- 200 of the Best Machine Learning, NLP, and Python Tutorials (2018) - Robbie Allen
** Most Coursera courses can be audited for free. From the enrollment page just choose the "audit" option when registering. When you audit a course you'll be able to see most of the course materials for free, but you won't be able to submit certain assessments or get grades for your work. You won't get a certificate, but you can pay for one at any time during or after the course. If you pay for a certificate, you may need to complete more coursework that wasn't available in the audit version.
*** Coursera specializations require a paid account, however you can enroll in the individual courses included in the specialization and audit those for free, see details above. To see the courses included in a specialization, click the "courses" tab on the specialization enrollment page.